Cyber Risk Monetization Using Asset-Level Vulnerability Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cybersecurity systems fail to accurately monetize cyber risks, leading to unprepared organizations in the face of cyber-attacks, and lack of proper evaluation during mergers, acquisitions, IPOs, and as financial KPIs, with insurance underwriting relying on high-level assessments.
Innovation Solution
A cybersecurity system that collects risk data from multiple sources, classifies vulnerabilities, and uses machine learning to estimate financial impacts of cyber-attacks, providing actionable financial insights and compliance recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If high-level risk assessment is used for cyber insurance underwriting, then underwriting process is simplified, but risk evaluation precision deteriorates
Solution Approach 1:
The system segments risk assessment into multiple hierarchical levels: enterprise-level aggregate risk scores and asset-level detailed vulnerability assessments. This allows insurance underwriters to efficiently evaluate overall enterprise risk while maintaining the capability to drill down into specific asset vulnerabilities when needed, thus balancing process simplicity with evaluation precision.
Solution Approach 2:
The system implements tiered assessment depth where not all enterprises receive the same level of detailed analysis. Standard underwriting uses aggregated risk scores for efficiency, while high-value or high-risk cases automatically trigger deeper asset-level vulnerability assessments. This partial application of detailed analysis optimizes the balance between operational efficiency and measurement precision.
2Measurement precision
If detailed vulnerability assessment is performed on all assets, then risk measurement precision is improved, but system complexity increases
Solution Approach 1:
The system applies different assessment depths to different assets based on their criticality and risk characteristics. Critical assets receive detailed vulnerability assessments with multiple data sources and analysis methods, while less critical assets use streamlined assessment protocols. This localized quality approach maintains high measurement precision for important assets without unnecessarily complicating the overall system.
Solution Approach 2:
The assessment system dynamically adjusts its complexity based on enterprise-specific factors such as industry sector, asset criticality, and detected risk levels. The system automatically scales the depth and breadth of vulnerability assessments, applying more complex analysis only where necessary, thus optimizing the balance between measurement precision and system complexity.
3Measurement precision
If comprehensive risk data collection from multiple sources is implemented, then risk evaluation accuracy is improved, but data processing time increases
Solution Approach 1:
The system pre-collects and normalizes risk data from multiple sources during routine operations and prior to assessment events. Vulnerability scans, threat intelligence feeds, and asset inventory data are continuously updated and prepared in advance, so that when risk assessment is triggered, the data is already structured and ready for analysis, significantly reducing processing time while maintaining comprehensive data coverage.
Solution Approach 2:
The system replaces manual data collection and processing mechanisms with automated electronic data gathering from integrated sources including vulnerability scanners, threat intelligence platforms, and asset management systems. This mechanical substitution with automated electronic processes enables comprehensive multi-source data collection without proportionally increasing processing time.
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A method and system for evaluating cyber risk of an entity comprising a risk evaluation module configured to collect risk data on risks of cyber-attacks connected to SaaS, infrastructure, and legal regulations classified by geolocation, industry type, and size of the victim organization, an entity evaluation module for collecting vulnerability data on assets of the entity classified by industry type, geolocation, size and cyber threat vector vulnerabilities and a monetization engine configured to make an assessment of expected financial loss from a specified cyber-attack to an entity classified by geolocation, industry type, and size, based on the risk data.